The Record surface-type transfer model

The MTRecord model implements the atom-resolved transfer model of Guinn, Pegram, Capp, Pollock, and Record (PNAS 2011), which explains why urea denatures proteins while glycine betaine (a protecting osmolyte) stabilizes them. Unlike the AutonBolen, MoeserHorinek, and Accessibility models, which are built from amino-acid transfer free energies (TFEs) split into backbone and side-chain contributions, the MTRecord model is parameterized directly on atomic surface types.

Motivation. Guinn et al. measured preferential interactions (osmometry and solubility) of urea and glycine betaine (GB) with 44 small model compounds (amino acids, peptides, sugars, polyols, aromatics, and salts), and fit the resulting chemical potential derivatives to a surface-additive model over seven coarse-grained atom/surface types: aliphatic C, aromatic C, hydroxyl O, amide O, carboxylate O, amide N, and cationic N. Because the surface types are transferable between model compounds and proteins, the resulting interaction potentials can be applied directly to a protein structure's atom-resolved solvent-accessible surface area (SASA), without ever forming an amino-acid-level TFE.

Fixed-state transfer free energies

The transfer free energy of a single (fixed) structure can be computed directly from its atom-resolved SASA with transfer_free_energy, by requesting the MTRecord model:

using PDBTools
native_state = read_pdb(PDBTools.MJC_NATIVE, "protein")
tfe = transfer_free_energy(native_state, "urea"; model=MTRecord)

TransferFreeEnergy{MTRecord} - 69 residues to 1M "urea".
    Total transfer free energy: -0.84033906 kcal mol⁻¹
    Backbone contributions: -0.37464893 kcal mol⁻¹
    Side-chain contributions: -0.46569017 kcal mol⁻¹

As with the other models, the result is split into backbone and side-chain contributions, here obtained by summing the atom-resolved surface-type contributions of backbone and side-chain atoms, respectively.

Denaturation m-values

Unlike the other transfer models, which pair a native structure with a parametric estimate of the denatured-state backbone/side-chain ASA (the Creamer model, see CreamerDenaturedModel), MTRecord is meant to be used with an explicit atomistic model of the unfolded state, since it needs a real $\Delta$ASA for every atom and surface type, not just per-residue backbone/side-chain totals. The unfolded-state reference used here is the fully extended (all-trans, $\phi = \psi = 180°$) chain, obtained with extended_chain - this is the same "extended β" reference state that Guinn et al. themselves use in their own validation (Table S3 of the paper).

MTRecordDenaturedModel wraps a protein's native structure together with its extended chain (built automatically) and the atom-resolved SASAs of both, computed once at construction:

MTRecordDenaturedModel

MTRecordDenaturedModel(atoms::AbstractVector{<:Atom})

mvalue(MTRecordDenaturedModel(prot), "urea")

mvalue(m::MTRecordDenaturedModel, cosolvent::AbstractString; alpha=1.0)

model = MTRecordDenaturedModel(native_state)
m = mvalue(model, "urea")

MValue{MTRecord} - 69 residues - cosolvent: "urea"
    Total m-value: -0.9539838 kcal mol⁻¹
    Backbone contributions: -0.34699118 kcal mol⁻¹
    Side-chain contributions: -0.6069926 kcal mol⁻¹

The m-value is obtained directly from Eq. 4, using the real, atom-by-atom $\Delta$ASA between the extended and native chains (extended minus native). By default (alpha=1.0), the extended-chain ASA is used as computed, with no adjustment; for urea, this reproduces the m-values reported in Table S3 of Guinn et al. well. Glycine betaine has no equivalent extended-chain validation set in the paper (Table S3 is urea-only), so predictions for "betaine" should be interpreted with more caution; the alpha keyword is available to empirically rescale the extended-chain ASA, if needed to better match experimental betaine data.

Model details

The additive model. Guinn et al.'s Eq. 4 expresses the chemical potential derivative $\mu_{23}/RT$ (essentially the preferential-interaction m-value per unit of exposed area) of a compound as a sum over surface types $i$, weighted by the corresponding ASA, plus an optional ionic term:

\[\frac{\mu_{23}}{RT} = \sum_i \alpha_i\, \text{ASA}_i + \sum_\text{ion} \nu_\text{ion}\, \beta_\text{ion}.\]

PDBTools uses only the surface-type sum (proteins are treated as neutral in this model). For a surface type $i$ and cosolvent, PDBTools.model_combination_rule returns the potential in cal mol⁻¹ Å⁻²:

\[\sigma_i = RT\,\alpha_i,\]

so that the contribution of a given atom to the transfer free energy (or m-value) is $\sigma_i \times \text{ASA}$ (or $\sigma_i \times \Delta\text{ASA}$ when comparing two states).

Surface-type assignment. PDBTools.record_surface_type maps each protein atom to one of the seven surface types based on element and residue/atom name, following the assignment used by Guinn et al.:

Sulfur (Met, Cys) and hydrogen atoms are ignored, since their contribution to the $\Delta$ASA of folding is negligible in the reference dataset. These surface types are also exposed as selection macro keywords, so that, for example, select(atoms, "record_aromatic_carbon") selects the atoms assigned to the aromatic-carbon surface type.

Interaction potentials. The values of $10^4\,\alpha_i$ (in m⁻¹ Å⁻²) currently implemented, from Table 1 of Guinn et al., are:

Only "urea" and "betaine" are currently supported, since these are the two cosolvents for which Guinn et al. report a full set of surface-type potentials.